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- Title
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.
- Authors
Ruffolo, Jeffrey A.; Chu, Lee-Shin; Mahajan, Sai Pooja; Gray, Jeffrey J.
- Abstract
Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold's capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures. Prediction of antibody structures is critical for understanding and designing novel therapeutic and diagnostic molecules. Here, the authors present IgFold: a fast, accurate method for antibody structure prediction using an end-to-end deep learning model.
- Subjects
LANGUAGE models; IMMUNOGLOBULINS; GENETIC recombination; DEEP learning; MONOCLONAL antibodies; GENETIC mutation; FORECASTING
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-38063-x